Abstract
Time series prediction problems are a difficult type of predictive modeling problem. In this paper, we propose a time series prediction method based on a variant long short-term memory (LSTM) recurrent neural network. In the proposed method, we firstly improve the memory module of the LSTM recurrent neural network by merging its forget gate and input gate into one update gate, and using Sigmoid layer to control information update. Using improved LSTM recurrent neural network, we develop a time series prediction model. In the proposed model, the parameter migration method is used model update to ensure the model has good predictive ability after predicting multi-step sequences. Experimental results show, compared with several typical time series prediction models, the proposed method have better performance for long-sequence data prediction.
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Notes
The dataset 1 and dataset 2 used in the experiment are come from the specific application.
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This work was supported by the National Natural Science Foundation of China under Grant No. 61772416; the National Major Research and Development Plan Program of China under Grant No. 2016YFB1001004; the Key Laboratory Project of the Education Department of Shaanxi Province under Grant No. 17JS098; Thirteenth Five-Year Equipment Pre-research Project No. 30503030201-02; the foundation of the State Key Laboratory of Astronautic Dynamics.
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Hu, J., Wang, X., Zhang, Y. et al. Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network. Neural Process Lett 52, 1485–1500 (2020). https://doi.org/10.1007/s11063-020-10319-3
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DOI: https://doi.org/10.1007/s11063-020-10319-3